Version 1
: Received: 17 July 2019 / Approved: 18 July 2019 / Online: 18 July 2019 (10:09:05 CEST)
How to cite:
Hossain, S.; Lee, D. Deep-Learning Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via Flying Robot with GPU-based Embedded Devices. Preprints2019, 2019070214
Hossain, S.; Lee, D. Deep-Learning Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via Flying Robot with GPU-based Embedded Devices. Preprints 2019, 2019070214
Hossain, S.; Lee, D. Deep-Learning Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via Flying Robot with GPU-based Embedded Devices. Preprints2019, 2019070214
APA Style
Hossain, S., & Lee, D. (2019). Deep-Learning Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via Flying Robot with GPU-based Embedded Devices. Preprints. https://doi.org/
Chicago/Turabian Style
Hossain, S. and Deok-Jin Lee. 2019 "Deep-Learning Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via Flying Robot with GPU-based Embedded Devices" Preprints. https://doi.org/
Abstract
In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one is designed using a Jetson TX or AGX Xavier, and the other is based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-art deep-learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep-learning-based association metric approach (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep-learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.
Keywords
Multi-Target Detection and Tracking; Multi-copter Drone; Aerial Imagery, Image Sensor, Deep Learning, GPU-based Embedded Module, Neural Computing Stick; Image Processing
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
